Neuroanatomical correlates of atrial fibrillation: a longitudinal MRI study.
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: To determine baseline volume and rate of volume change of whole brain, hippocampus, and entorhinal cortex in patients with atrial fibrillation. METHODS: We analyzed clinical and neuroimaging data collected as part of Alzheimer's Disease Neuroimaging Initiative in the United States and Canada. Patients with atrial fibrillation were identified based on baseline clinical/cognitive assessments, and age and gender-matched controls without atrial fibrillations were selected (1:1 ratio). All participants underwent 1.5 T structural magnetic resonance imaging (MRI) at specified intervals (6 or 12 months) for 2-3 years. RESULTS: A total of 33 persons with atrial fibrillation were included. There was no difference in whole brain and ventricular volumes at baseline MRI between cases and controls. There was significantly lower entorhinal cortex volume on right (p = 0.01) and left (p = 0.01) sides in patients with atrial fibrillation. There was significantly lower volume for middle temporal lobes on right (p = 0.04) and left (p = 0.001) sides. The rate of progression of atrophy in entorhinal cortex and middle temporal lobes was not different between patients with atrial fibrillation and controls. CONCLUSIONS: The association of atrial fibrillation with volume loss in entorhinal cortex and middle temporal lobes may provide new insights into pathophysiology of atrial fibrillation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".